Mass Spectrometry has been widely used to identify materials present in a sample for a variety of applications. However, real-time analysis of spectral data has proven to be very challenging due to the high level of processing required for accurate spectral deconvolution. Mass spectral analysis of data generated using various techniques that include electrospray ionization (also referred to as “ESI”) or laser spray ionization techniques has been particularly challenging because they typically produce ions with the same isotope profiles being detected at multiple charge states due to multiple charging of the analyte molecules. This has generally limited the utility of mass spectral analysis to those applications that do not require having to analyze data in real-time.
The term “real-time” as used herein typically refers to reporting, depicting, or reacting to events at substantially the same rate and sometimes at substantially the same time as they unfold, rather than delaying a report or action. For example, a “substantially same” rate and/or time may include some small difference from the rate and/or time at which the events unfold. In the present example, real-time reporting or action could be also described as “close to”, “similar to”, or “comparable to” to the rate and/or time at which the events unfold.
Real-time spectral deconvolution, material identification, and reporting are important for a number of reasons. One reason includes the fact that the answers generated are useful to guide decisions that are time sensitive. Some decisions include additional analysis of the subject material that can be made during the same analysis process that produced the original spectral information for the material. For example, the ability to provide real-time decision making power is particularly important in clinical settings where patient outcomes can be significantly improved.
ESI is a technique widely used in Mass Spectrometry applications for producing ion species from macromolecules. In typical applications, analytes of interest are dissolved in a liquid solution and sprayed through an ESI emitter with an electrical potential to produce charged droplets. The droplets carry a charge that, in combination with the effects of solvent evaporation causes production of gas phase ions that include analytes with various charge states. The ions advance to other regions of the mass spectrometer for analysis.
Similarly, with laser spray ionization multiply charged ions can be formed when a sample, fixed to a glass slide and covered with matrix (e.g. 2,5-dihydroxyacetophenone), is struck with a laser pulse from the back of the slide. The resulting ions from the ionization plum are then transferred into the mass spectrometer using an electrical potential. In some cases, laser spray ionization has better efficiency than ESI and ion abundances can be orders of magnitude greater. For example, some embodiments of laser spray ionization provide a better representation of the solution-phase characteristics of certain types of biomolecules or combinations of biomolecules (e.g. protein-DNA interactions).
Recently, advancements in the field of mass spectrometry isotope profile modeling of biological (or polymeric) samples and fitting have made real-time spectral deconvolution more feasible. The first advancement includes the concept of what is sometimes referred to as “Averagine”. The Averagine approach produces approximations of the isotope profile models as a function of mass by estimating the elemental composition of the compounds. Examples of the Averagine approach are described in Senko et al., 1995, JASMS, titled “Determination of monoisotopic masses and ion populations for large biomolecules from resolved isotopic distributions”, which is hereby incorporated by reference herein in its entirety for all purposes.
A second advancement includes use of isotope look-up tables and charge state determinations that includes an automated fitting process at large scale by fast charge state determination and pre-caching the isotope profiles in look-up tables. One example includes what is sometimes referred to as the “THRASH” algorithm described by Horn et al., 2000, JASMS, titled “Automated reduction and interpretation of high resolution electrospray mass spectra of large molecules”, which is hereby incorporated by reference herein in its entirety for all purposes.
A third advancement includes characterizing isotope profiles that were either overlapping by charge (e.g. as described by Zhang et al., 1997, JASMS, titled “A universal algorithm for fast and automated charge state deconvolution of electrospray mass-to-charge ratio spectra, which is hereby incorporated by reference herein in its entirety for all purposes), or intensity (e.g. as described by Renard, 2008, BMC bioinformatics, titled “NITPICK Peak identification for mass spectroscopy data”; or Kronewitter, 2012, Proteomics, titled “The Glycolyzer automated glycan annotation software for high performance mass spectrometry and it application to ovarian cancer glycan biomarker discovery”, each of which is hereby incorporated by reference herein in its entirety for all purposes).
Lastly, a fourth advancement included use of exact elemental composition instead of the Averagine approach to develop isotope profile models. The elemental composition approach utilizes knowledge of the material elemental composition a priori to generate one or more isotope profile models for the material (e.g. as described by Kronewitter, 2014, Anal. Chem., titled “GlyQ-IQ glycomics quintavariate-informed quantification with high-performance computing and GlycoGrid 4D visualization”, which is hereby incorporated by reference herein in its entirety for all purposes).
In general, the previously described approaches calculate the isotope profiles at run time or perform simple array look-ups of pre-calculated profiles. Unfortunately the previous approaches are too slow and limited in terms of the ability to identify a material from a large pool of candidates while mass spectral information from other materials is being acquired by a mass spectrometer.
Therefore, it is highly desirable to have an analysis approach that substantially increases the speed and performance of processing by a computer in order to provide accurate real-time identification and quantification of compounds for a wide range of applications. For example, increased processing performance completes each task more rapidly thereby freeing up processing resources for other real-time computing tasks that enables rapid and accurate identification and quantification.